Toward a More Personalized MOOC: Data Analysis to Identify Drinking Water Production Operators’ Learning Characteristics—An Ecuador Case

• Published in: Sustainability Vol. 14; no. 21; p. 14206
• Main Authors: Bustamante-León, Martín, Herrera, Paúl, Domínguez-Granda, Luis, Schellens, Tammy, Goethals, Peter L. M., Alejandro, Otilia, Valcke, Martin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Certification; Cluster analysis; Customization; Drinking water; Education; Instructional design; International organizations; Knowledge; Learning; Massive open online courses; Online education; Operators; Principal components analysis; Quality assurance; Quality control; Quality management; Quality standards; Secondary schools; Statistical methods; Students; Surveys; Sustainability; Training; Water quality; Water treatment; Ecuador; United States > US; California
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su142114206

Only 35% of the Ecuadorian population consumes drinking water of “assured quality”. One of the causes is related to the deficiencies in the technical ability of the operators due to their lack of education, technical training, and experience. Massive open online courses (MOOCs) responsive to characteristics and learning needs are an option to strengthen the skills of operators. The goal of the present study is therefore to describe a methodology that includes the application of a survey and the use of statistical methods such as categorical principal component analysis (CATPCA) and cluster analysis to identify and assess learning characteristics. The results present the most frequent variables in the personal, academic, emotional, social, and cognitive aspects. They also show the preferences and learning needs of the operators. Finally, it is concluded that this study identifies common learning characteristics, needs, and preferences that are relevant for the creation of a quality personalized instructional design in MOOCs.